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Robust smoothing: Smoothing parameter selection and applications to fluorescence spectroscopy

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  • Lee, Jong Soo
  • Cox, Dennis D.

Abstract

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented.

Suggested Citation

  • Lee, Jong Soo & Cox, Dennis D., 2010. "Robust smoothing: Smoothing parameter selection and applications to fluorescence spectroscopy," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3131-3143, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:3131-3143
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    References listed on IDEAS

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    1. Hee‐Seok Oh & Doug Nychka & Tim Brown & Paul Charbonneau, 2004. "Period analysis of variable stars by robust smoothing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 15-30, January.
    2. Hee-Seok Oh & Douglas W. Nychka & Thomas C. M. Lee, 2007. "The Role of Pseudo Data for Robust Smoothing with Application to Wavelet Regression," Biometrika, Biometrika Trust, vol. 94(4), pages 893-904.
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    Cited by:

    1. Oliver Morell & Dennis Otto & Roland Fried, 2013. "On robust cross-validation for nonparametric smoothing," Computational Statistics, Springer, vol. 28(4), pages 1617-1637, August.
    2. Marie Hušková & Matúš Maciak, 2017. "Discontinuities in robust nonparametric regression with α-mixing dependence," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 447-475, April.
    3. Gutiérrez, Luis & Gutiérrez-Peña, Eduardo & Mena, Ramsés H., 2014. "Bayesian nonparametric classification for spectroscopy data," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 56-68.
    4. Yuan Xue & Xiangrong Yin, 2015. "Sufficient dimension folding for a functional of conditional distribution of matrix- or array-valued objects," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(2), pages 253-269, June.

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